The Web is increasingly centered around the collective effort of the crowds, with contributions ranging from user-generated content and social media to Citizen Science projects and crowd-based peer-grading and peer-learning in online education. But every crowdsourcing based system relies on users actually participating and making high-quality contributions to function effectively. How can we design systems so that self-interested users —  with their own costs and benefits to participation —  are properly incentivized to participate and contribute with high effort?

We discuss a game-theoretic framework for incentive design in crowdsourcing, where potential contributors are modeled as self-interested agents who strategically choose whether or not to participate, and how much effort to expend, in response to the incentives offered by the system. We illustrate the game-theoretic approach via the problem of learning the qualities of online content from viewer feedback: here, a learning algorithm needs to not only quickly identify the best contributions, but also simultaneously create incentives for attention-motivated users to make high-quality contributions, leading to a multi-armed bandit problem where the number and success probabilities of the arms of the bandit are endogenously determined in response to the learning algorithm.